From 4a3dd0ff61c424b0eede2024aedb1aa8e5a54fbb Mon Sep 17 00:00:00 2001
From: Julien Jerphanion <git@jjerphan.xyz>
Date: Mon, 24 Jan 2022 10:02:12 +0100
Subject: [PATCH] MAINT Do not compute distances for uniform weighting

---
 sklearn/neighbors/_classification.py | 17 +++++++++++++++--
 sklearn/neighbors/_regression.py     |  8 +++++++-
 2 files changed, 22 insertions(+), 3 deletions(-)

diff --git a/sklearn/neighbors/_classification.py b/sklearn/neighbors/_classification.py
index 4f84a16211dbd..bcad8c71aee07 100644
--- a/sklearn/neighbors/_classification.py
+++ b/sklearn/neighbors/_classification.py
@@ -213,7 +213,14 @@ def predict(self, X):
         y : ndarray of shape (n_queries,) or (n_queries, n_outputs)
             Class labels for each data sample.
         """
-        neigh_dist, neigh_ind = self.kneighbors(X)
+        if self.weights == "uniform":
+            # In that case, we do not need the distances to perform
+            # the weighting so we do not compute them.
+            neigh_ind = self.kneighbors(X, return_distance=False)
+            neigh_dist = None
+        else:
+            neigh_dist, neigh_ind = self.kneighbors(X)
+
         classes_ = self.classes_
         _y = self._y
         if not self.outputs_2d_:
@@ -255,7 +262,13 @@ def predict_proba(self, X):
             The class probabilities of the input samples. Classes are ordered
             by lexicographic order.
         """
-        neigh_dist, neigh_ind = self.kneighbors(X)
+        if self.weights == "uniform":
+            # In that case, we do not need the distances to perform
+            # the weighting so we do not compute them.
+            neigh_ind = self.kneighbors(X, return_distance=False)
+            neigh_dist = None
+        else:
+            neigh_dist, neigh_ind = self.kneighbors(X)
 
         classes_ = self.classes_
         _y = self._y
diff --git a/sklearn/neighbors/_regression.py b/sklearn/neighbors/_regression.py
index 74cecede2efa4..423d01612b514 100644
--- a/sklearn/neighbors/_regression.py
+++ b/sklearn/neighbors/_regression.py
@@ -228,7 +228,13 @@ def predict(self, X):
         y : ndarray of shape (n_queries,) or (n_queries, n_outputs), dtype=int
             Target values.
         """
-        neigh_dist, neigh_ind = self.kneighbors(X)
+        if self.weights == "uniform":
+            # In that case, we do not need the distances to perform
+            # the weighting so we do not compute them.
+            neigh_ind = self.kneighbors(X, return_distance=False)
+            neigh_dist = None
+        else:
+            neigh_dist, neigh_ind = self.kneighbors(X)
 
         weights = _get_weights(neigh_dist, self.weights)